Extract Apical progenitors and compute Pseudo-DorsoVentral score

Extract apical progenitors and filter the gene expression matrix

We perform Kmeans clustering on the 2 cell state scores :

  • Apical progenitors AP
  • Basal progenitors BP

We then extract the glutamatergic neuron branche as beeing the Kmeans cluster with the highest mean Apical progenitor signature

Import Spring dimensionality reduction

Cell cycle associated genes were excluded for PCA dimensionality reduction and Spring plot was generating with these parameter :

Number of cells: 1648
Number of genes that passed filter: 857
Min expressing cells (gene filtering): 3
Min number of UMIs (gene filtering): 3
Gene variability %ile (gene filtering): 90
Number of principal components: 7
Number of nearest neighbors: 20
Number of force layout iterations: 500

Fit a principal curve over the AP in the Spring space

## Starting curve---distance^2: 2022449778
## Iteration 1---distance^2: 745172
## Iteration 2---distance^2: 696270.5
## Iteration 3---distance^2: 677509.1
## Iteration 4---distance^2: 668251.7
## Iteration 5---distance^2: 663393.6
## Iteration 6---distance^2: 660928.6
## Iteration 7---distance^2: 659586.4
## Iteration 8---distance^2: 658917.8
## Iteration 9---distance^2: 658638.8

Manuscript Fig. 5A

Manuscript Fig. 5A

Find differentially expressed genes along the pseudo DV axis

Cluster gene by similar profiles over pseudo-DV axis

Assign domain identity

We assign domain identity based on clusters’ transcriptional profile by setting boundaries over pseudo-dv score

Manuscript Fig. 5D

Manuscript Fig. 5D

Transfert these ident on the full dataset (Manuscript Fig. 2A)

## [1] "Cluster_Sub.Pallium.2: 363 Cells"
## [1] "Cluster_Ventral.Pallium: 312 Cells"
## [1] "Cluster_Sub.Pallium.3: 197 Cells"
## [1] "Cluster_lateral.Pallium.1: 219 Cells"
## [1] "Cluster_Dorsal.Pallium: 201 Cells"
## [1] "Cluster_lateral.Pallium.2: 113 Cells"
## [1] "Cluster_Sub.Pallium.1: 241 Cells"
Manuscript Fig. 2A

Manuscript Fig. 2A

Session Info

## [1] "05 novembre, 2020, 18,16"
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] splines   stats4    parallel  stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] viridis_0.5.1       viridisLite_0.3.0   wesanderson_0.3.6  
##  [4] RColorBrewer_1.1-2  gridExtra_2.3       reshape_0.8.8      
##  [7] dplyr_0.8.3         ggExtra_0.9         cluster_2.1.0      
## [10] princurve_2.1.4     monocle_2.14.0      DDRTree_0.1.5      
## [13] irlba_2.3.3         VGAM_1.1-2          Biobase_2.46.0     
## [16] BiocGenerics_0.32.0 Seurat_2.3.4        Matrix_1.2-17      
## [19] cowplot_1.0.0       ggplot2_3.2.1      
## 
## loaded via a namespace (and not attached):
##   [1] snow_0.4-3           backports_1.1.5      Hmisc_4.3-0         
##   [4] plyr_1.8.4           igraph_1.2.5         lazyeval_0.2.2      
##   [7] densityClust_0.3     fastICA_1.2-2        digest_0.6.25       
##  [10] foreach_1.4.7        htmltools_0.5.0      lars_1.2            
##  [13] gdata_2.18.0         magrittr_1.5         checkmate_1.9.4     
##  [16] gclus_1.3.2          mixtools_1.1.0       ROCR_1.0-7          
##  [19] limma_3.42.0         matrixStats_0.55.0   R.utils_2.9.0       
##  [22] docopt_0.6.1         colorspace_1.4-1     ggrepel_0.8.1       
##  [25] xfun_0.18            sparsesvd_0.2        crayon_1.3.4        
##  [28] jsonlite_1.7.0       zeallot_0.1.0        survival_2.44-1.1   
##  [31] zoo_1.8-6            iterators_1.0.12     ape_5.3             
##  [34] glue_1.4.1           registry_0.5-1       gtable_0.3.0        
##  [37] kernlab_0.9-29       prabclus_2.3-1       DEoptimR_1.0-8      
##  [40] scales_1.1.0         pheatmap_1.0.12      bibtex_0.4.2        
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##  [52] proxy_0.4-23         mclust_5.4.5         SDMTools_1.1-221.1  
##  [55] Formula_1.2-3        tsne_0.1-3           htmlwidgets_1.5.1   
##  [58] httr_1.4.1           FNN_1.1.3            gplots_3.0.1.1      
##  [61] fpc_2.2-3            acepack_1.4.1        modeltools_0.2-22   
##  [64] ica_1.0-2            farver_2.0.1         pkgconfig_2.0.3     
##  [67] R.methodsS3_1.7.1    flexmix_2.3-15       nnet_7.3-14         
##  [70] labeling_0.3         tidyselect_0.2.5     rlang_0.4.7         
##  [73] reshape2_1.4.3       later_1.0.0          munsell_0.5.0       
##  [76] tools_3.6.3          ggridges_0.5.1       fastmap_1.0.1       
##  [79] evaluate_0.14        stringr_1.4.0        yaml_2.2.1          
##  [82] npsurv_0.4-0         knitr_1.26           bit64_4.0.2         
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##  [88] purrr_0.3.3          RANN_2.6.1           dendextend_1.12.0   
##  [91] pbapply_1.4-2        nlme_3.1-141         mime_0.7            
##  [94] slam_0.1-46          R.oo_1.23.0          hdf5r_1.3.2.9000    
##  [97] compiler_3.6.3       rstudioapi_0.11      png_0.1-7           
## [100] lsei_1.2-0           tibble_2.1.3         stringi_1.4.6       
## [103] highr_0.8            lattice_0.20-41      HSMMSingleCell_1.6.0
## [106] vctrs_0.2.0          pillar_1.4.2         lifecycle_0.1.0     
## [109] combinat_0.0-8       Rdpack_0.11-0        lmtest_0.9-37       
## [112] data.table_1.12.6    bitops_1.0-6         seriation_1.2-9     
## [115] gbRd_0.4-11          httpuv_1.5.2         R6_2.4.1            
## [118] latticeExtra_0.6-28  TSP_1.1-10           promises_1.1.0      
## [121] KernSmooth_2.23-15   codetools_0.2-16     MASS_7.3-53         
## [124] gtools_3.8.1         assertthat_0.2.1     withr_2.1.2         
## [127] qlcMatrix_0.9.7      mgcv_1.8-33          diptest_0.75-7      
## [130] doSNOW_1.0.18        grid_3.6.3           rpart_4.1-15        
## [133] tidyr_1.0.0          class_7.3-17         rmarkdown_2.5       
## [136] segmented_1.0-0      Rtsne_0.15           shiny_1.4.0         
## [139] base64enc_0.1-3

  1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France

---
title: "Investigation of the apical progenitors diversity"
author:
  - Matthieu Moreau^[Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France], matthieu.moreau@inserm.fr
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_document: 
    code_download: yes
    df_print: tibble
    highlight: haddock
    includes:
      in_header: header.html
    theme: cosmo
    toc: yes
    toc_depth: 5
    toc_float:
      collapsed: yes
---

```{css, echo=FALSE}
h1 {
  font-size: 34px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #e64d00;
  text-decoration: none;
}
h1.title {
  font-size: 40px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  text-align: center;
  text-decoration: none;
  color: #000000;
}
h2 {
  font-size: 30px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h3 {
  font-size: 24px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h4 {
  font-size: 20px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h5 {
  font-size: 18px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}

.scroll-100 {
  max-height: 200px;
  overflow-y: auto;
  background-color: inherit;
}

p {
  font-size: 16px;
}
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.align = 'center', message=FALSE, warning=FALSE)
```

# Load libraries and QCFiltered dataset

```{r }
# Load library
library(Seurat)
library(monocle)
library(princurve)
library(cluster)
library(parallel)
library(ggplot2)
library(ggExtra)
library(dplyr)
library(reshape)
library(gridExtra)
library(RColorBrewer)
library(wesanderson)
library(viridis)

#Set ggplot theme as classic
theme_set(theme_classic())
```

```{r}
# Load the full annotated dataset
Allcells.data <- readRDS("./QC.filtered.cells.RDS")
```

# Extract Apical progenitors and compute Pseudo-DorsoVentral score

## Extract apical progenitors and filter the gene expression matrix

We perform Kmeans clustering on the 2 cell state scores :

- Apical progenitors `AP`
- Basal progenitors `BP`

```{r }
set.seed(100)
#K-means clustering based on AP, BP scores across cells
cl <- kmeans(cbind(Allcells.data@meta.data$AP_signature1, Allcells.data@meta.data$BP_signature1), 3)
Allcells.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)
```


```{r fig.dim=c(5.3, 4)}
col.pal <- wes_palette("GrandBudapest1", 3, type = "discrete")

p1 <- ggplot(Allcells.data@meta.data, aes(x=AP_signature1, y=BP_signature1, colour = kmeanClust)) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none")
ggMarginal(p1, type = "histogram", fill="lightgrey") ; rm(p1)

DimPlot(Allcells.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F)
```

We then extract the glutamatergic neuron branche as beeing the Kmeans cluster with the highest mean `Apical progenitor` signature

```{r fig.dim=c(5.3, 4)}
#Fin cluster wiht the highest mean APscore
MeanKclust.APscore <- aggregate(AP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
APclust <- MeanKclust.APscore %>% filter(AP_signature1 == max(AP_signature1)) %>% pull(kmeanClust)

#Extract apical progenitors 
barcodes <- Allcells.data@meta.data %>% filter(kmeanClust == APclust) %>% pull(Barcodes)
AP.data <-  SubsetData(Allcells.data, cells.use = barcodes , subset.raw = T,  do.clean = F)

#Further filter the 3 outlier cells based on spring coordinates
cells <- rownames(AP.data@dr$spring@cell.embeddings[AP.data@dr$spring@cell.embeddings[,2] > 250,])
AP.data <-  SubsetData(AP.data, cells.use = cells , subset.raw = T,  do.clean = F)

DimPlot(AP.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F)
```

## Filter gene counts matrix

```{r}
#Remove non epressed genes
num.cells <- Matrix::rowSums(AP.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 20)])
AP.data@raw.data <- AP.data@raw.data[genes.use, ]
AP.data@data <- AP.data@data[genes.use, ]

#Normalize and Scale the data
AP.data <- NormalizeData(object = AP.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(AP.data@meta.data$nUMI)),
                         display.progress = F)

AP.data <- FindVariableGenes(object = AP.data,
                                  mean.function = ExpMean,
                                  dispersion.function = LogVMR,
                                  x.low.cutoff = 0.0125,
                                  x.high.cutoff = 3,
                                  y.cutoff = 1, do.plot = F,
                                  display.progress = F)

AP.data <- ScaleData(object = AP.data, vars.to.regress = c("CC.Difference","percent.mito", "nUMI"), display.progress = F)
```

## Import Spring dimensionality reduction

**Cell cycle associated genes** were excluded for PCA dimensionality reduction and Spring plot was generating with these parameter :

```
Number of cells: 1648
Number of genes that passed filter: 857
Min expressing cells (gene filtering): 3
Min number of UMIs (gene filtering): 3
Gene variability %ile (gene filtering): 90
Number of principal components: 7
Number of nearest neighbors: 20
Number of force layout iterations: 500
```

```{r}
#Import Spring coordinates calculated without cell cycle genes
Coordinates <- read.table("./Progenitors/E12.AP.Coordinates.txt", sep=",", header = F)[,c(2,3)]
rownames(Coordinates) <- rownames(AP.data@meta.data)
AP.data <- SetDimReduction(AP.data,
                           reduction.type = "spring.AP",
                           slot = "cell.embeddings",
                           new.data = as.matrix(Coordinates))
AP.data@dr$spring.AP@key <- "spring.AP"
colnames(AP.data@dr$spring.AP@cell.embeddings) <- paste0(GetDimReduction(object = AP.data, reduction.type = "spring.AP",slot = "key"), c(1,2))

```

## Fit a principal curve over the AP in the Spring space

```{r}
data <- data.frame(springAP.1 = AP.data@dr$spring.AP@cell.embeddings[,1],
                   springAP.2 = AP.data@dr$spring.AP@cell.embeddings[,2],
                   spring1 = AP.data@dr$spring@cell.embeddings[,1],
                   spring2 = AP.data@dr$spring@cell.embeddings[,2])

# Fit the principal curve
fit <- principal_curve(as.matrix(data[,1:2]),
                       smoother='lowess',
                       trace=TRUE,
                       f = 0.7,
                       stretch=0,
                       plot_iterations = F)
```

```{r}
DorsoVentral.Score <- fit$lambda/max(fit$lambda) #The actual speudotime
pc.line <- as.data.frame(fit$s[order(fit$lambda),]) #The principal curve smoothed

data$Phase <- as.character(AP.data@meta.data$Phase)
data$DorsoVentral.Score <- DorsoVentral.Score

# Direction of the maturation score using Zbtb20 expression (reverte if positive correlation)
if (cor(data$DorsoVentral.Score, AP.data@data['Zbtb20', ]) > 0) { data$DorsoVentral.Score <- -(data$DorsoVentral.Score - max(data$DorsoVentral.Score))}

AP.data@meta.data$DorsoVentral.Score <- data$DorsoVentral.Score
```

```{r fig.dim=c(5.3, 4)}
#Plot Cell onto PC1 and PC2 with principal curve
ggplot(data, aes(springAP.1, springAP.2)) + 
  geom_point(aes(color=Phase), size=2, shape=16, values=col.pal) +
  geom_line(data=pc.line, color='red', size=0.77)
```

```{r fig.dim=c(5.3, 4), fig.cap= "Manuscript Fig. 5A"}
#Plot Speudotime color gradient on the cell cycle filtered Spring embbeding
ggplot(data, aes(springAP.1, springAP.2)) +
  geom_point(aes(color=DorsoVentral.Score), size=2, shape=16) +
  scale_color_viridis(direction = -1) +
  geom_line(data=pc.line, color='red', size=0.77)
```

```{r fig.dim=c(5.3, 4)}
#Plot Speudotime color gradient on the Spring embbeding calculated from full dataset
ggplot(data, aes(spring1, spring2)) + 
  geom_point(aes(color=DorsoVentral.Score), size=2, shape=16) +
  scale_color_viridis(direction = -1) 
```

# Find differentially expressed genes along the pseudo DV axis

## Initialize a monocle object

```{r}
# Transfert metadata 
meta.data <- data.frame(barcode = rownames(AP.data@meta.data),
                        Cluster = AP.data@meta.data$old.ident,
                        DorsoVentral.Score =  AP.data@meta.data$DorsoVentral.Score,
                        CellcyclePhase = AP.data@meta.data$Phase,
                        row.names = rownames(AP.data@meta.data))
                   
Annot.data  <- new('AnnotatedDataFrame', data = meta.data)

# Transfert count data
count.data = data.frame(gene_short_name = rownames(AP.data@raw.data),
                  row.names = rownames(AP.data@raw.data))

feature.data <- new('AnnotatedDataFrame', data = count.data)

# Create the CellDataSet object
gbm_cds <- newCellDataSet(as.matrix(AP.data@raw.data),
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 1,
                          expressionFamily = negbinomial())
```


```{r}
gbm_cds <- estimateSizeFactors(gbm_cds)
gbm_cds <- estimateDispersions(gbm_cds)
gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)
```

```{r}
rm(list = ls()[!ls() %in% c("AP.data", "gbm_cds")])
```

## Test each gene trend over pseudo-DV score

```{r}
# Exclude cell cycle associated genes
CCgenes <- as.character(read.table("./Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])
Input.genes <- AP.data@var.genes[!AP.data@var.genes %in% CCgenes]
```


```{r}
# Perform the test for differential expression as a function of pseudo-DV score while controling for cell cycle phase
DV.Axis.genes <- differentialGeneTest(gbm_cds[Input.genes,], 
                                      fullModelFormulaStr = "~sm.ns(DorsoVentral.Score, df = 3)*CellcyclePhase", 
                                      reducedModelFormulaStr = "~CellcyclePhase", 
                                      cores = detectCores() -2)

# Filter genes with a FDR < 0.001
DV.Axis.genes.FDR.filtered <- DV.Axis.genes %>% filter(qval < 1e-3)
```


## Smooth significative gene expression pseudo-DV axis

```{r}
# Create a new pseudo-DV vector of 500 points
nPoints <- 500
new_data <- data.frame(DorsoVentral.Score = seq(min(pData(gbm_cds)$DorsoVentral.Score), max(pData(gbm_cds)$DorsoVentral.Score), length.out = nPoints))

# Smooth gene expression
Smooth.curve.matrix <- genSmoothCurves(gbm_cds[as.character(DV.Axis.genes.FDR.filtered$gene_short_name),],
                                       trend_formula = "~sm.ns(DorsoVentral.Score, df = 3)",
                                       relative_expr = TRUE,
                                       new_data = new_data,
                                       cores= detectCores() - 2)
```

# Cluster cell by similar expression profiles on the pseudo-DV axis

```{r}
set.seed(100)
# Cluster cells using the Partitioning Around Medoids algorithm
Cells.Clust <- pam(as.dist((1 - cor(Smooth.curve.matrix ,method = "spearman"))), k=7)
Domaines.Clust <- data.frame(Domaines = paste0("Clust.",Cells.Clust$clustering))
```

# Cluster gene by similar profiles over pseudo-DV axis

## Perform hierarchical clustering

```{r}
# Build genes dendrogram
dst <- dist(t(scale(t(Smooth.curve.matrix))), method = "manhattan")
hc <- hclust(dst, method = "ward.D") #"ward.D"

# Seriate the dendrogram
dend <- as.dendrogram(hc)
dend <- dendextend::seriate_dendrogram(dend, dst, method="GW")

# Find 15 cluster based on hierarchical clustering
clusters <- cutree(hc, k=10)

Gene.dynamique <- data.frame(Gene= names(clusters),
                             pval= DV.Axis.genes.FDR.filtered$pval,
                             qval=DV.Axis.genes.FDR.filtered$qval,
                             num_cells_expressed=DV.Axis.genes.FDR.filtered$num_cells_expressed,
                             Waves = as.numeric(clusters)) %>% arrange(Waves)

row.names(Gene.dynamique) <- Gene.dynamique$Gene
Gene.dynamique$Gene.Clusters <- paste0("Clust.",Gene.dynamique$Waves)

write.table(Gene.dynamique, "./Progenitors/Gene.dynamique.csv", sep = ";", quote = F)
```


## Plot gene/cell heatmap

```{r fig.dim=c(7, 9.3), fig.cap= "Manuscript Fig. 5C"}
anno.colors <- list(Domaines = c(Clust.1="#83c3b8", Clust.2="#009fda", Clust.3="#3e69ac", Clust.4="#e46b6b", Clust.5="#e3c148", Clust.6="#b7d174", Clust.7="#68b041"),
                    Gene.Clusters = c(Clust.1 ="#ec756d" , Clust.2="#ebcb2e", Clust.3="#b79f0b", Clust.4="#7293c8", Clust.5="#3ca73f",
                                      Clust.6="#9ec22f", Clust.7="#cc3a1b", Clust.8="#4cabdc", Clust.9="#cc8778", Clust.10="#4784a2"))

pheatmap::pheatmap(Smooth.curve.matrix,
                   cluster_rows= as.hclust(dend),
                   scale = "row",
                   cluster_cols = F,
                   gaps_col = cumsum(as.numeric(table(Domaines.Clust$Domaines))),
                   annotation_row = Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_col = Domaines.Clust,
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color = rev(brewer.pal(11,"RdBu")),
                   breaks = seq(-2.5,2.5, length.out = 11),
                   main = "Genes expression along Dorso-Ventral axis")
```
##Plot gene clusters trends
```{r fig.dim=c(6, 9), fig.cap= "Manuscript Fig. 5C"}
source("./functions/GenesTrendPlots.R")
Clusters.trend(AP.data,
               Which.cluster = 1:10,
               clust.list = clusters,
               group.by = "global",
               span = 1,
               Smooth.method = "auto",
               Use.scale.data = T)
```

## Assign domain identity


We assign domain identity based on clusters' transcriptional profile by setting boundaries over pseudo-dv score
```{r}
# Set the boundary over speudotime score
new_data$cluster <- Domaines.Clust$Domaines
Infered.Domain.boundary <- aggregate(DorsoVentral.Score ~ cluster, new_data, max) %>% pull(DorsoVentral.Score)

# Assign identity based on the position of the cell on the pseudo-dv axis
Domaine.Ident <- sapply(AP.data@meta.data$DorsoVentral.Score,
                        function(x){ if(x<Infered.Domain.boundary[1]){ x = "Sub.Pallium.1"
                        } else if(x> Infered.Domain.boundary[1] & x< Infered.Domain.boundary[2]){ x ="Sub.Pallium.2"
                        } else if(x> Infered.Domain.boundary[2] & x< Infered.Domain.boundary[3]){ x = "Sub.Pallium.3" 
                        } else if(x> Infered.Domain.boundary[3] & x< Infered.Domain.boundary[4]){ x = "Ventral.Pallium"
                        } else if(x> Infered.Domain.boundary[4] & x< Infered.Domain.boundary[5]){ x = "lateral.Pallium.1"
                        } else if(x> Infered.Domain.boundary[6]){ x = "Dorsal.Pallium"
                        } else x="lateral.Pallium.2"})

# Transfert the identity to the Seurat object
AP.data@meta.data$Domaine <- Domaine.Ident
AP.data <- SetAllIdent(AP.data, id = "Domaine")
```


```{r fig.dim=c(10,5), fig.cap= "Manuscript Fig. 5D"}
Plot.Genes.trend(AP.data,
                 genes = c("Gsx2", "Dbx1", "Gm29260", "Tfap2c", "Emx1", "Lrrn1"),
                 Use.scale.data = F)
```

```{r fig.dim=c(5.3, 4)}
DimPlot(AP.data,
         group.by = "Domaine",
         reduction.use = "spring",
         dim.1 = 1,
         dim.2 = 2,
         do.label=F,
         label.size = 4,
         no.legend = F,
         cols.use = tolower(c("#68B041", "#E3C148", "#B7D174", "#83C3B8", "#009FDA", "#3E69AC", "#E46B6B"))
         )
```

# Plot Figure 5B

```{r}
#load full dataset
Allcells.data <- readRDS("./QC.filtered.cells.RDS")

#Transfer the identities
Rename.Clust <-  function(Clustdata, RawQCdata) {
  unClustered.cells <- RawQCdata@meta.data$Barcodes
  RawQCdata <- SetIdent(RawQCdata, cells.use = unClustered.cells, ident.use = "All.Unclustered.Cells")
  
  for(i in unique(Clustdata@meta.data$Domaine)){
  New.ident <- i
  Barcodes <- rownames(subset(Clustdata@meta.data, Clustdata@meta.data$Domaine == i))
  print(paste0("Cluster_",i,": ",length(Barcodes), " Cells"))
  Barcodes <- Barcodes[Barcodes %in% rownames(RawQCdata@meta.data)]
  RawQCdata <- SetIdent(RawQCdata, cells.use = Barcodes ,ident.use = paste0("AP.",i))
  }
  return(RawQCdata)
}

Allcells.data <- Rename.Clust(Clustdata = AP.data, RawQCdata = Allcells.data)
```

```{r fig.dim=c(5.3, 4), , fig.cap= "Manuscript Fig. 5B"}
colors <-  c("#969696",tolower(c("#68B041", "#E3C148", "#B7D174", "#83C3B8", "#009FDA", "#3E69AC", "#E46B6B")))

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors)
```

```{r}
rm(list = ls()[!ls() %in% c("AP.data")])
```

# Plot the representative gene of the Figure S6

```{r}
# Load custom plotting functions
source("./functions/GenesTrendPlots.R")
```

## Cluster 6

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6A"}
Plot.Genes.trend(AP.data,
                 genes = c("Dlx1", "Ano1", "Dlx2", "Olig2"),
                 Use.scale.data = F)
```

## Cluster 2

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6B"}
Plot.Genes.trend(AP.data,
                 genes = c("Otx2", "Zbtb20", "Six3", "Ascl1"),
                 Use.scale.data = F)
```

## Cluster 3

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6C"}
Plot.Genes.trend(AP.data,
                 genes = c("Meis1", "Rorb", "Epha3"),
                 Use.scale.data = F)
```

## Cluster 1

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6D"}
Plot.Genes.trend(AP.data,
                 genes = c("Sfrp2", "Eva1c", "Sema5a", "Fat4"),
                 Use.scale.data = F)
```

## Cluster 9

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6E"}
Plot.Genes.trend(AP.data,
                 genes = c("Dmrta2","Lypd6", "Pax6", "Dmrta1"),
                 Use.scale.data = F)
```

## Cluster 5 ?

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6F"}
Plot.Genes.trend(AP.data,
                 genes = c("Emx1", "Arx", "Lmo3"),
                 Use.scale.data = F)
```

## Cluster 9

```{r fig.dim=c(10,6), fig.cap= "Manuscript Fig.S6G"}
Plot.Genes.trend(AP.data,
                 genes = c("Fezf2", "Emx2", "Sp8"),
                 Use.scale.data = F)
```

# Transfert these ident on the full dataset (Manuscript Fig. 2A)

```{r}
#load full dataset
Allcells.data <- readRDS("./Clustered.cells.RDS")

#Transfer the identities
Rename.Clust <-  function(Clustdata, RawQCdata) {
  for(i in unique(Clustdata@meta.data$Domaine)){
    New.ident <- i
    Barcodes <- rownames(subset(Clustdata@meta.data, Clustdata@meta.data$Domaine == i))
    print(paste0("Cluster_",i,": ",length(Barcodes), " Cells"))
    Barcodes <- Barcodes[Barcodes %in% rownames(RawQCdata@meta.data)]
    RawQCdata <- SetIdent(RawQCdata, cells.use = Barcodes ,ident.use = paste0("AP.",i))
  }
  return(RawQCdata)
}

Allcells.data <- Rename.Clust(Clustdata = AP.data, RawQCdata = Allcells.data)
```

```{r fig.dim=c(8, 6), fig.cap= "Manuscript Fig. 2A"}
colors2 <-  c("#969696",
              tolower(c("#68B041", "#E3C148", "#B7D174", "#83C3B8", "#009FDA", "#3E69AC", "#E46B6B")),
              "#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#3ca73f","#31b6bd",
              "#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors2)
```

```{r}
Allcells.data <- StashIdent(Allcells.data, save.name = "Cluster.ident")
saveRDS(Allcells.data, "./Clustered.cells.RDS")
saveRDS(AP.data, "./AP.data.RDS")
```

# Session Info
```{r}
#date
format(Sys.time(), "%d %B, %Y, %H,%M")

#Packages used
sessionInfo()
```